pmlpp/mlpp/knn/knn.cpp

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//
// kNN.cpp
//
// Created by Marc Melikyan on 10/2/20.
//
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#include "knn.h"
#include "../lin_alg/lin_alg.h"
#include "../utilities/utilities.h"
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#include <algorithm>
#include <iostream>
#include <map>
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MLPPKNN::MLPPKNN(std::vector<std::vector<double>> inputSet, std::vector<double> outputSet, int k) :
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inputSet(inputSet), outputSet(outputSet), k(k) {
}
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std::vector<double> MLPPKNN::modelSetTest(std::vector<std::vector<double>> X) {
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std::vector<double> y_hat;
for (int i = 0; i < X.size(); i++) {
y_hat.push_back(modelTest(X[i]));
}
return y_hat;
}
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int MLPPKNN::modelTest(std::vector<double> x) {
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return determineClass(nearestNeighbors(x));
}
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double MLPPKNN::score() {
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Utilities util;
return util.performance(modelSetTest(inputSet), outputSet);
}
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int MLPPKNN::determineClass(std::vector<double> knn) {
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std::map<int, int> class_nums;
for (int i = 0; i < outputSet.size(); i++) {
class_nums[outputSet[i]] = 0;
}
for (int i = 0; i < knn.size(); i++) {
for (int j = 0; j < outputSet.size(); j++) {
if (knn[i] == outputSet[j]) {
class_nums[outputSet[j]]++;
}
}
}
int max = class_nums[outputSet[0]];
int final_class = outputSet[0];
for (int i = 0; i < outputSet.size(); i++) {
if (class_nums[outputSet[i]] > max) {
max = class_nums[outputSet[i]];
}
}
for (auto [c, v] : class_nums) {
if (v == max) {
final_class = c;
}
}
return final_class;
}
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std::vector<double> MLPPKNN::nearestNeighbors(std::vector<double> x) {
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LinAlg alg;
// The nearest neighbors
std::vector<double> knn;
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std::vector<std::vector<double>> inputUseSet = inputSet;
//Perfom this loop unless and until all k nearest neighbors are found, appended, and returned
for (int i = 0; i < k; i++) {
int neighbor = 0;
for (int j = 0; j < inputUseSet.size(); j++) {
bool isNeighborNearer = alg.euclideanDistance(x, inputUseSet[j]) < alg.euclideanDistance(x, inputUseSet[neighbor]);
if (isNeighborNearer) {
neighbor = j;
}
}
knn.push_back(neighbor);
inputUseSet.erase(inputUseSet.begin() + neighbor); // This is why we maintain an extra input"Use"Set
}
return knn;
}